Esker, a provider of document process automation solutions and SAP software solution and technology partner, has announced it has reached an agreement with Multiradio S.A., an Argentina-based telecommunications company. Multiradio has selected Esker’s cloud-based Accounts Payable solution to integrate with its existing SAP application in an effort to automate its vendor invoicing process.
Multiradio handles approximately 1,400 vendor invoices each month, with the average document being eight pages. Esker’s Accounts Payable solution will be leveraged as an on-demand automation service to integrate with Multiradio’s SAP system, effectively streamlining every phase of vendor invoice processing.
“When we realized our AP process was getting too complex, the goal was to consolidate the process using a cloud solution — Esker had everything we were looking for in terms of SAP integration and workflow,” said Monica Melito, CFO at Multiradio. “We considered going the BPO route but, through my involvement with ASUG, I knew of Esker’s experience with cloud computing. We couldn’t have asked for a better solution for our first on-demand project.”
Whitepapers
Related reading
Central banks best suited to issue digital currencies
By Aaran Fronda A recent report by the Official Monetary and Financial Institutions Forum (OMFIF) said that central banks rather than private ... read more
Instant payments: innovations inbound for corporates
In 2020, instant payments look set to continue their current trajectory to become the biggest trend in payments. While these schemes already offer numerous benefits to corporates, leveraging innovations such as APIs and request to pay will go some way to unlocking their full potential, argues Michael Knetsch
Obstacles exist for banks to meet ECB’s instant payments goal
The cost of joining instant payment platforms will be one of many hurdles banks and payment services providers must overcome to meet ... read more
Banks must be aware of “biases” in data used to train ML models
Financial institutions need to be conscious of biases in the historical data that is being used to train machine learning (ML) models, ... read more